Personal Identification System Research Articles

Framework for multiple person identification using YOLOv8 detector: a transfer learning approach

Video surveillance extensively uses person detection and tracking technology based on video. The majority of person detection and classification techniques currently in use encounter challenges in video sequences brought on by occlusion, ambient lighting, and variations in human facial position. This paper proposed an effective person identification and classification system based on deep learning, which comprises a you only look once at version 8 (YOLOv8) detection and classification model, to classify human faces in video sequences accurately. This work proposes a new staff-detection and classification (S-DEC) dataset for comprehensive performance evaluation. visual tracker benchmark (VTB) standard database is used for performance comparison with the proposed S-DEC dataset. The proposed technique achieved 98.67% precision accuracy. For the S-DEC dataset, the system gave 94.67% accuracy in identifying facial images from a video sequence of 38 people addressing the pose variation occlusion challenge. Earlier methods used to provide approximately 85% to 90% results taking more execution time. Many existing techniques were successful in detecting people only-identification of the detected person has been done in limited papers. The proposed method uses the cross-stage partial connections (CSPDarknet53) model, integrated with YOLOv8, to achieve faster results. The proposed framework took 35 minutes to train a deep learning model. A testing time of 2 minutes ensured that the proposed framework outplayed other existing methodologies and successfully identified extra information about the detected person.

Intelligent Localization System for Rescue during the Accident in Construction Work

A significant development in assuring the security and well-being of laborers in the construction sector is the Intelligent Localization System for Construction Accident Rescue employing RSSI for Person Identification and Health Monitoring System. During emergencies or accidents, this system is intended to precisely find and identify individuals within a building site by utilizing the power of Radio Signal Strength Indicator (RSSI) technology. The system precisely locates each worker by evaluating the strength of the RSSI signal, facilitating quick and effective rescue efforts. Additionally, the system has health monitoring features that offer workers real-time insights into their well-being. This entails keeping an eye on critical indicators like body temperature and heart rate in order to recognize crisis signals early and seek medical attention quickly if necessary. The combination of individuals.

IMPLEMENTATION OF AN IDENTIFICATION SYSTEM WITH FACIAL IMAGE PROCESSING (EIGENFACE) USING MATLAB APPLICATION

Facial recognition has emerged as a prominent personal identification system, especially in the industrial sector where traditional attendance card machines have been prevalent. However, manual systems pose several drawbacks, including susceptibility to fraud, lack of flexibility, and resource wastage in card production. To address these issues, this research proposes a shift from card-based attendance systems to a facial recognition-based system using MATLAB application. The study aims to design an identification system based on the eigenface method to process facial images. It begins with a thorough literature review to gather relevant references. Subsequently, a specialized MATLAB application was developed for face identification, and its accuracy was tested. The research utilizes trained data consisting of 45 photos and tested data consisting of 15 photos to evaluate the system's accuracy. The test results reveal a 100% accuracy rate in system identification. Notably, the accuracy varies when identifying faces with different background images, indicating the robustness of the MATLAB application. Overall, the findings suggest that MATLAB can effectively implement an image processing attendance system using the eigenface method.

Effective Technique for Face Recognition System Using CNN in Attendance Applications

Face recognition is a personal identification system that uses individual characteristics of a person to recognize the person's identity. The software which is used for face recognition is MATLAB. The project area is image processing. Different types of ordinal measures are derived from magnitude and phase components of Gabor images. Two orientations and two wavelengths are used. Gabor filters are used in the pre-processing step. Array of Gabor filters is called as filter bank. Convolutional Neural Network (CNN) is used for face recognition, which outperforms the traditional techniques. It has three layers in which Convolution Layer is used for feature extraction. Pooling Layer is used, which crops the particular part of the image containing the object and then resizes it, because classification requires fixed size of an image. SoftMax Layer is used to normalize the un-normalized vectors and make the predictions into the probabilistic forms. Loss function is used to figure out the error. Finally, the face of a student is recognized by using CNN.

Forensic Examination of Fingerprint Patterns among Different Generations in South Indian Families

Fingerprint patterns are unique and reliable for identification. This research paper focuses on a comparative analysis to determine the inheritance of fingerprint patterns within Indian families. The sample collection process for this comparative analysis involved working with 10 families. The study aims to gain insights into the hereditary aspects of fingerprint characteristics among the Indian population. The research methodology involves the collection of fingerprints and the analysis of patterns using microscopes, magnifying lenses, and software. The results reveal both class and individual characteristics within fingerprints, contributing to our understanding of dermatoglyphics. The average class characteristics percentage totals around 71.5%, with an average individual characteristic percentage of approximately 13.6%. The research has implications for forensic investigations, genetics, and personal identification systems. Further studies with larger sample sizes and genetic analysis integration are recommended for future research.

Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.

Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals. A ballistocardiogram with a piezoelectric sensor was tested using seven individuals. The frequency spectra of the biosignals acquired from the piezoelectric sensors exhibited multiple peaks corresponding to the harmonics originating from the heartbeat. We aimed for individual identification based on the shapes of these peaks as the recognition criteria. The results of individual identification using deep learning techniques revealed good identification proficiency. Altogether, the monitoring system integrated with piezoelectric sensors showed good potential as a personal identification system for identifying individuals with abnormal biological signals.

Person identification with arrhythmic ECG signals using deep convolution neural network

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person’s beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy.

SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition

Deep neural networks have empowered accurate device-free human activity recognition, which has wide applications. Deep models can extract robust features from various sensors and generalize well even in challenging situations such as data-insufficient cases. However, these systems could be vulnerable to input perturbations, i.e. adversarial attacks. We empirically demonstrate that both black-box Gaussian attacks and modern adversarial white-box attacks can render their accuracies to plummet. In this paper, we firstly point out that such phenomenon can bring severe safety hazards to device-free sensing systems, and then propose a novel learning framework, SecureSense, to defend common attacks. SecureSense aims to achieve consistent predictions regardless of whether there exists an attack on its input or not, alleviating the negative effect of distribution perturbation caused by adversarial attacks. Extensive experiments demonstrate that our proposed method can significantly enhance the model robustness of existing deep models, overcoming possible attacks. The results validate that our method works well on wireless human activity recognition and person identification systems. To the best of our knowledge, this is the first work to investigate adversarial attacks and further develop a novel defense framework for wireless human activity recognition in mobile computing research.

Blood group determination using fingerprint

The fingerprint pattern stands out as the most authentic and unique characteristic defining human identity. This unique pattern is immutable and persists unaltered until an individual's demise. In various circumstances, particularly in legal proceedings, fingerprint evidence is highly regarded. The distinctive minutiae pattern of each person is unparalleled, with the probability of resemblance being exceedingly low, nearly one in sixty-four thousand million. This distinctiveness holds true even for identical duplet. The individualistic ridge pattern persists unchanged from birth, serving as a constant aspect of personal identity. This paper presents a method involving the comparison of specific feature patterns derived from fingerprints for personal identification systems. Fingerprint data is employed in the investigation of blood group determination as well. In the process of fingerprint matching, ridge frequency is assessed, and spatial features are extracted using a Gabor filter for this specific purpose. Consequently, blood group determination can be performed using fingerprint analysis.

Appearance and Pose-Conditioned Human photograph generation using Deformable GANs

In this study, we address the challenge of generating images of individuals based on pose and appearance data. Specifically, we take an image, xa, of an individual and a target pose, P (xb), extracted from another image, xb. We then generate a new image of the same individual in the target pose, P (xb), while preserving the visual details from xa. To manage pixel-to-pixel misalignments caused by pose differences between P (xa) and P (xb), we incorporate deformable skip connections in our Generative Adversarial Network’s generator. Additionally, we propose a nearest-neighbour loss as an alternative to the standard L1 and L2 losses to match the texture of the generated image with the target image. Our approach demonstrates competitive quantitative and qualitative results using standard datasets and protocols recently proposed for this task. We also carry out a comprehensive evaluation using off-the-shelf person-identification (Re-id) systems trained with person-generation-based augmented data, a key application for this task. Our experiments reveal that our Deformable GANs can significantly boost Re-id accuracy, surpassing data-augmentation techniques specifically trained using Re-identification losses. index Terms—Conditional GAN, Image Generation, Deformable Objects, Human Pose.

FaceBreach Alert: Multi-Modal Intrusion Detection emphasizing Face Recognition

Abstract: The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the muchstudied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisher face method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is MATLAB software.

A system and method for person identification and positioning incorporating object edge detection and scale-invariant feature transformation

During the overhead crane, due to the complex background and large image size, the direct processing algorithm usually leads to time-consuming operation and difficult to achieve accurate personnel positioning. To address these issues, we propose the feature points recognition and matching algorithm incorporating object edge detection and scale-invariant feature transform (SIFT), which is applied to the designed object detection and positioning algorithm for binocular stereo vision. The proposed method applies the Canny operator to extract contours from the locally cropped regions obtained using YOLOv5. The contour points are used as the key points of SIFT, and corresponding feature points are created descriptors, and feature points matching are performed. Experimental results indicate that the proposed method can filter feature points successfully. Moreover, the binocular stereo vision system with the binocular positioning algorithm designed has good performance, with the overall depth direction positioning error within 5% in a range of 12 m.

Human Recognition Using Ear Features: A Review

Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as DCT, DWT, DFT, PCA, LBP, SURF, SIFT, etc., or deep learning techniques such as CNN, DNN, Alex Net CNN, VGG-16, SVM, Squeeze Net, Google Net, MobileNetV2, etc. The effort will make it easier for researchers, especially those who are new to the field, to have a brief understanding of the trend of employing deep learning in a trustworthy biometric for the identification and recognition of human identification.

Realtime face matching and gender prediction based on deep learning

<span lang="EN-US">Face analysis is an essential topic in computer vision that dealing with human faces for recognition or prediction tasks. The face is one of the easiest ways to distinguish the identity people. Face recognition is a type of personal identification system that employs a person’s personal traits to determine their identity. Human face recognition scheme generally consists of four steps, namely face detection, alignment, representation, and verification. In this paper, we propose to extract information from human face for several tasks based on recent advanced deep learning framework. The proposed approach outperforms the results in the state-of-the-art.</span>

Research on the early warning and prevention system of infectious diseases based on large cruise ships

In order to improve the ability of large cruise ships to deal with infectious disease epidemics and reduce the losses caused by outbreaks on cruise ships, this project has designed the design of infectious disease early warning and prevention and control systems for large luxury cruise ships. The system consists of a large cruise ship infected person identification and tracking system, an infectious disease prevention and isolation system, and an infectious disease treatment system. It can quickly identify and isolate suspected infected persons on large cruise ships, and at the same time, it can reduce the ability of the virus to spread in the air, effectively reduce the probability of outbreaks of infectious diseases on cruise ships, and ensure the health of boarding personnel.

Victim identification through skeletal remains: A review

Investigative authorities use forensic anthropology to examine human skeletal remains in order to identify unidentified human remnants. Anthropology alone is the study of man, but also includes culture, language, and physical remains of humans. Forensic anthropology is used to help with the recovery of human remains and to interpret trauma. Asian, African, and European people, as well as the living and the dead, are all of interest to anthropologists. Anthropologists are also fascinated with a wide range of human characteristics, such as their technology, family dynamics, cultures, and linguistic. Forensic otology is a branch of forensic anthropology. The field began in the 19th century. Mathieu or fill published a textbook on measurements of arm bones in 1835. E.A Bertillon proposed an anthropometric system for personal identification in 1885. A branch of physical anthropology is forensic anthropology. In this analysis, criminal cases are solved using skeletal analysis and other archaeology methods. Experts in forensic anthropology focus on examining hard tissues like bones. They are also skilled at locating and recovering buried bodies. In this review we are going to discuss about the ways of identification of a victim by found skeletal remains

Privacy-Aware Gait Identification With Ultralow-Dimensional Data Using a Distance Sensor

As one of the most natural user behaviors, walking has been widely focused on developing personal identification systems due to its unique biometric authentication features. Popular visual solutions are usually affected by various environmental conditions, and their redundant user information (e.g., body type and appearance) makes it more challenging for users to maintain privacy and security. This paper proposes a distance sensor–based gait identification system that uses only one-dimensional data with a simple system structure. Specifically, a time-of-flight sensor was placed in front of a walking person, and a time series of distances was acquired. We extracted gait features from the data by calculating the velocity and acceleration curves and identifying individuals using a random forest classifier. We evaluated our system on 10 users using leave-one-out cross-validation. The average identification accuracy was 91.05% for 10 users. This study shows that gait recognition is possible using only one-dimensional time-series data with a noncontact sensor. It can be used as a contactless identification, reducing the computational resources required for low-cost and low-power consumption edge computing.

Age invariant face recognition using discriminative tensor subspace learning with fuzzy based classification

AbstractAging is a complex process that affects both the shape and texture of a face. In the aging domain, various methods have been proposed for age estimation or simulation of the face based on an image at a given age. Face recognition methods that are designed for automatic face recognition are still a challenging issue. We present a novel age invariant face recognition method using tensor subspace learning with fuzzy synthetic classification. Local Binary Pattern (LBP) processed face images are used to learn the tensor sub‐space, which converts high dimensional feature space to age‐invariants while retaining key elements of the local geometrical structure of the face. Tensor sub‐space is better in terms of sub‐space learning and feature extraction than Principal Component Analysis (PCA)/Linear Discriminant Analysis (LDA) that been used in many techniques in the literature. Tensor Normalized Face images are generated which exhibit maximum inter‐class distances and minimum intra‐class distances. Further local patches are used for synthesizing global Fuzzy membership scores to classify the test face images. Experiments performed on standard face‐aging datasets, namely FG‐NET, AGEDB, and MORPH‐Album‐II, and received accuracy of 99.15%, 99.20%, and 99.8%, respectively. Experimental results outperform the current state‐of‐the‐art techniques, and results show the promise of the proposed system for personal identification despite the aging process. It also proved that the local descriptor gives better performance over the global descriptor like PCA for the aging process. The method also demonstrated improved performance as compared with compute‐intensive methods that required training on deep networks.

Classroom Attendance using Face Detection and Facial Organs

The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisherface method. Facial image recognition Eigenface method is based on the reduction of face-dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest Eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is matlab software.

RFID MATERIAL Based Electric Vehicle Charging Station

The variety of EVs on the road aspect is increased; hence the charging stations in necessary in every entire parking buildings and individual provider branch will turn out to be additional dominant. These stations can is accountable for assembly the necessities of the supply grid, EV proprietors, and parking building operators. With an elevated numbs of electrical automobiles (EVs) on the streets, charging communications is gaining an entire extra crucial role in the entire together gathering the desires of the nearby supply grid and of EV users. The emission of the damaging CO2 inside the surroundings from the conventional gas-based vehicles is one of the essential elements for the upward push and fame of electric cars (EVs) within the fresh elegant towns within the current years. In this context, EVs offer better surroundings within the contemporary elegant city. But there exist many demanding situations inclusive of elegant charging, path scheduling, information dissemination etc., for the usage of EVs inside the fresh-day elegant metropolis. So, there's a need for the new strategies and solutions to improve the performance of the existing charging machine of the EVs. Maintaining awareness on these points, this task proposes a new approach for the electrical automobile wireless charging the use of RFID MATERIAL tags together with an optimized path making plans to have a foremost charging value. This proposed gadget RFID MATERIAL system for person identification and charging authorization as part of a elegant charging infrastructure offering rate tracking and manage. The RFID MATERIAL presents a cost-efficient method to perceive and consent to motors for charging and would permit EV charging to is carried out successfully while gazing grid constraints and meeting the needs of EV drivers. After RFID MATERIAL reader initialization, RFID MATERIAL tag study via RFID MATERIAL reader with help of percent microcontroller and RFID MATERIAL tag information are displayed within the liquid crystal display. The automobile charging is based to the entire y on amount/money and robotic the entire y debited from account with assist of RFID MATERIAL and keypad. On this gadget we can monitor our charging degree the usage of IOT module.